Machine learning approach to predict postoperative opioid requirements in ambulatory surgery patients.

Machine learning approach to predict postoperative opioid requirements in ambulatory surgery patients.

Publication date: Apr 26, 2020

Opioids play a critical role in acute postoperative pain management. Our objective was to develop machine learning models to predict postoperative opioid requirements in patients undergoing ambulatory surgery. To develop the models, we used a perioperative dataset of 13,700 patients (? 18 years) undergoing ambulatory surgery between the years 2016-2018. The data, comprising of patient, procedure and provider factors that could influence postoperative pain and opioid requirements, was randomly split into training (80%) and validation (20%) datasets. Machine learning models of different classes were developed to predict categorized levels of postoperative opioid requirements using the training dataset and then evaluated on the validation dataset. Prediction accuracy was used to differentiate model performances. The five types of models that were developed returned the following accuracies at two different stages of surgery: 1) Prior to surgery-Multinomial Logistic Regression: 71%, Na”ive Bayes: 67%, Neural Network: 30%, Random Forest: 72%, Extreme Gradient Boost: 71% and 2) End of surgery-Multinomial Logistic Regression: 71%, Na”ive Bayes: 63%, Neural Network: 32%, Random Forest: 72%, Extreme Gradient Boost: 70%. Analyzing the sensitivities of the best performing Random Forest model showed that the lower opioid requirements are predicted with better accuracy (89%) as compared with higher opioid requirements (43%). Feature importance (% relative importance) of model predictions showed that the type of procedure (15.4%), medical history (12.9%) and procedure duration (12.0%) were the top three features contributing to model predictions. Overall, the contribution of patient and procedure features towards model predictions were 65% and 35% respectively. Machine learning models could be used to predict postoperative opioid requirements in ambulatory surgery patients and could potentially assist in better management of their postoperative acute pain.

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Concepts Keywords
Ambulatory Opioid
Opioid Morphine
Pain Statistics
Pain Management Artificial intelligence
Perioperative Machine learning
Random Forest Surgery
Pain management
Multinomial logistic regression
Surgery
Random forest
Perioperative
Neural Network

Semantics

Type Source Name
drug DRUGBANK Flunarizine
drug DRUGBANK Tropicamide
drug DRUGBANK Trestolone
disease MESH infection
disease MESH thromboembolism
disease MESH respiratory depression
disease MESH pruritus
disease MESH psychological distress
disease MESH suffering
disease MESH depression
disease MESH development
disease MESH anxiety
disease MESH Chronic pain
drug DRUGBANK Acetylsalicylic acid
disease MESH anomalies
disease MESH Renal
disease MESH Sleep apnea
disease MESH Alcohol abuse
disease MESH Drug abuse
disease MESH traumatic stress disorder
disease MESH PTSD
disease MESH confusion
drug DRUGBANK Ethanol
drug DRUGBANK Morphine
disease MESH Risk factors
drug DRUGBANK Acetaminophen
disease MESH Stroke
disease MESH systolic heart failure
disease MESH hepatitis
disease MESH postoperative complications
drug DRUGBANK Tramadol

Original Article

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